Machine Learning
Dense vs. MoE Models: Key Tradeoffs Developers Must Know
Exploring dense vs. Mixture-of-Experts (MoE) models for AI: when to prioritize simplicity or scalability, with insights on performance and memory trade-offs.
NVIDIA FLARE Expands Federated Learning with Kubernetes, Slurm
NVIDIA FLARE 2.9 adds Slurm support, enabling scalable federated learning across heterogeneous infrastructures like Docker, Kubernetes, and HPC clusters.
Ray Summit 2026 Highlights AI Advances in Reinforcement Learning
Ray Summit 2026 showcased cutting-edge AI breakthroughs, from NVIDIA's RL scaling to Lila Sciences' physical AI pipelines. Here's what mattered.
NVIDIA Jetson Enables Local AI with Compact Models
NVIDIA Jetson now supports compact AI models like Nemotron 3.5 Lightning, enabling powerful reasoning capabilities directly at the edge.
Google's Gemini Models Launch Agentic Video Understanding
Google's Gemini 3.7 Flash introduces agentic video understanding, reducing costs by 66% and token usage by 88% for video analysis.
NVIDIA TensorRT Model Connect Simplifies AI Deployment
NVIDIA's TensorRT Model Connect enables AI model deployment from checkpoint to inference in two commands, bridging open models to production.
How Learning Loops Build Durable AI Moats
Learning loops connect data curation, model training, and inference to help companies differentiate AI systems and scale intelligence efficiently.
Harvey Launches Tenet, Open-Weight Legal AI Model
Harvey debuts Tenet, its first post-trained open-weight model, promising improved legal AI performance and cost-efficiency for law firms.
NVIDIA AVO Hits 100% on ARC-AGI-3 Benchmark, Redefining AI Agents
NVIDIA's AVO achieves 100% efficiency on ARC-AGI-3, showcasing a breakthrough in long-horizon autonomous agent systems.
NVIDIA Optimizes JAX LLM Training with Host Offloading
NVIDIA's host offloading for JAX LLM training boosts GPU memory efficiency, enabling larger batch sizes and faster throughput.